I am a research scientist at Scaled Cognition and a member of the initial team, where we are developing a new generation of rational, controllable AI models deployable as domain experts for grounded, real-world applications.

Previously, I was a Senior Principal Researcher at Microsoft Semantic Machines, where I led the design and development of a system that aims to predict the next user action (e.g., sending an email, opening a file, adding specific people to a calendar invite, etc.) based on what a user and others in their network have been doing (underlying this system was a novel Foundation Model over user actions). I managed a team across both research and engineering, and helped set the technical direction for the broader organization. I invested heavily in aligning research and engineering, and I worked closely with multiple partner teams at Microsoft to deliver on our objectives, while also helping improve the underlying platform and infrastructure that we leveraged. Earlier on in my career at Microsoft I also served as a key contributor to the research and development of the Semantic Machines conversational AI platform which handled user requests by using a neural network to synthesize computer programs in a new proprietary programming language. I designed and shipped the program synthesis neural network (i.e., a contextual semantic parser) that powers the platform.

Prior to that, I was a PhD student in the Machine Learning Department of the School of Computer Science at Carnegie Mellon University. My advisor was Tom Mitchell and I worked on Never-Ending Learning. My PhD thesis on learning collections of functions can be found here. Throughout my PhD I also worked on multiple other projects related to artificial intelligence and machine learning.

Before I joined CMU, I graduated with an M.Eng. in Electrical and Electronic Engineering from Imperial College London. For my Master's thesis I proposed a way to use topic modelling methods in order to perform human motion classification.